Minimum Communication Cost Research Articles

Optimal measurements for nonlocal correlations

A problem in quantum information theory is to find the experimental setup that maximizes the nonlocality of correlations with respect to some suitable measure such as the violation of Bell inequalities. The latter has however some drawbacks. First and foremost it is unfeasible to determine the whole set of Bell inequalities already for a few measurements and thus unfeasible to find the experimental setup maximizing their violation. Second, the Bell violation suffers from an ambiguity stemming from the choice of the normalization of the Bell coefficients. An alternative measure of nonlocality with a direct information-theoretic interpretation is the minimal amount of classical communication required for simulating nonlocal correlations. In the case of many instances simulated in parallel, the minimal communication cost per instance is called nonlocal capacity, and its computation can be reduced to a convex-optimization problem. This quantity can be computed for a higher number of measurements and turns out to be useful for finding the optimal experimental setup. Focusing on the bipartite case, in this paper, we present a simple method for maximizing the nonlocal capacity over a given configuration space and, in particular, over a set of possible measurements, yielding the corresponding optimal setup. Furthermore, we show that there is a functional relationship between Bell violation and nonlocal capacity. The method is illustrated with numerical tests and compared with the maximization of the violation of CGLMP-type Bell inequalities on the basis of entangled two-qubit as well as two-qutrit states. Remarkably, the anomaly of nonlocality displayed by qutrits turns out to be even stronger if the nonlocal capacity is employed as a measure of nonlocality.

Communication and cooling aware job allocation in data centers for communication-intensive workloads

Energy consumption is an increasingly important concern in data centers. Today, nearly half of the energy in data centers is consumed by the cooling infrastructure. Existing policies on thermally-aware workload allocation do not consider applications that include many tasks (or threads) running on a large set of nodes with significant communication among the tasks. Such jobs, however, constitute most of the cycles in high performance computing (HPC) domain, and have started to appear in other data centers as well. Job allocation strongly affects the performance of such communication-intensive applications. Communication-aware job allocation methods exist, but they focus solely on performance and do not consider cooling energy. This paper proposes a novel job allocation methodology to jointly minimize communication cost and cooling energy consumption in data centers. We formulate and solve the joint optimization problem using binary quadratic programming. Our joint optimization algorithm reduces cooling energy by 16.4% on average with only a 2.66% average increase in application running time compared to solely performance-aware allocations. To further optimize the communication cost, we develop a Charm++ based framework that extracts the communication behavior of applications. We then integrate our job allocation policy with recursive coordinate bisection (RCB) based task mapping method to place highly-communicating tasks in close proximity. Experimental results show that task mapping further decreases the communication cost by up to 20.9% compared to assuming all-to-all communication, a popular assumption in much of the prior work.

Relay Placement in Wireless Networks: Minimizing Communication Cost

Given $n$ source nodes and $k$ relay nodes, we model the optimal relay topology problem allowing for simultaneous optimization of the relay node locations and traffic through the network, so that the overall number of packet retransmissions is minimized. We argue that state-of-the-art models and algorithms for relay placement in wireless networks do not reflect salient characteristics of the optimal relays topology and lead to suboptimal solutions. We do not constrain the position of relays to a finite set of discrete points, as the latter may not be feasible in practical networks. In this case, we show that just listing a set of feasible sites for the relays is already at least $APX$ -hard. Exploiting convexity in a special case of the network communication cost function, we give an optimal algorithm for the relay placement problem. However, the algorithm is exponential on the number of nodes in the network. We suggest a practical heuristic algorithm for relay placement: RePlace . We compare RePlace numerically to the optimal algorithm and show that RePlace achieves the optimal or almost optimal solutions. We implement RePlace in the full network stack simulator JiST/SWANS. The relay topologies generated by RePlace eliminate overhead communication cost almost entirely.

Simple algorithm for computing the communication complexity of quantum communication processes

A two-party quantum communication process with classical inputs and outcomes can be simulated by replacing the quantum channel with a classical one. The minimal amount of classical communication required to reproduce the statistics of the quantum process is called its communication complexity. In the case of many instances simulated in parallel, the minimal communication cost per instance is called the asymptotic communication complexity. Previously, we reduced the computation of the asymptotic communication complexity to a convex minimization problem. In most cases, the objective function does not have an explicit analytic form, as the function is defined as the maximum over an infinite set of convex functions. Therefore, the overall problem takes the form of a minimax problem and cannot directly be solved by standard optimization methods. In this paper, we introduce a simple algorithm to compute the asymptotic communication complexity. For some special cases with an analytic objective function one can employ available convex-optimization libraries. In the tested cases our method turned out to be notably faster. Finally, using our method we obtain 1.238 bits as a lower bound on the asymptotic communication complexity of a noiseless quantum channel with the capacity of 1 qubit. This improves the previous bound of 1.208 bits.

A high-performance parallel algorithm for nonnegative matrix factorization

Non-negative matrix factorization (NMF) is the problem of determining two non-negative low rank factors W and H , for the given input matrix A , such that A ≈ WH . NMF is a useful tool for many applications in different domains such as topic modeling in text mining, background separation in video analysis, and community detection in social networks. Despite its popularity in the data mining community, there is a lack of efficient distributed algorithms to solve the problem for big data sets. We propose a high-performance distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems for W and H . It maintains the data and factor matrices in memory (distributed across processors), uses MPI for interprocessor communication, and, in the dense case, provably minimizes communication costs (under mild assumptions). As opposed to previous implementations, our algorithm is also flexible: (1) it performs well for both dense and sparse matrices, and (2) it allows the user to choose any one of the multiple algorithms for solving the updates to low rank factors W and H within the alternating iterations. We demonstrate the scalability of our algorithm and compare it with baseline implementations, showing significant performance improvements.

Robust Dynamic Event-triggered Control for Linear Uncertain System

Abstract Event-triggered control has a greater significance in networked control system (NCS) as it minimizes communication cost. The present paper introduces a novel event-based robust control framework for linear uncertain system. The robust control law is designed by solving an optimal control problem and realized thorough a dynamic event-triggering mechanism to reduce the communication traffic. A dynamic variable is used to generate the event-triggering law using Input-to-State Stability (ISS) criteria. Proposed control strategies ensure stability in the presence of bounded system uncertainties. Derivation of dynamic event-triggering rule with a non-zero positive inter-event time and corresponding stability criteria for uncertain system are the key contributions of this paper. The validation of proposed algorithm is carried out through a numerical simulation.

A New Data Transmission Strategy in Mobile D2D Networks --- Deterministic, Greedy, or Planned Opportunistic Routing?

In this paper, we study the problem of delay-constrained data transmission in mobile opportunistic device-to-device networks. In contrast to the deterministic or greedy single-copy single-path (SCSP) and multicopy multipath (MCMP) routing schemes that have been discussed in the literature, we develop a planned opportunistic routing scheme that aims to determine the optimal single-copy multipath (SCMP) transmission strategy that satisfies the delay requirement and, at the same time, minimizes communication cost. We first address the unicast by formulating the optimization problem and developing a distributed routing algorithm under practical network settings. Then, we explore optimal multicast strategies based on the SCMP transmissions. We implement the proposed algorithms on Android tablets and carry out extensive experiments, each with 25 nodes, for a period of two weeks. Moreover, we extract the algorithm codes from our prototype and run simulations based on the Haggle trace to study performance trends under various network settings. The experimental and simulation results show that the proposed protocols achieve significant performance gain in comparison with their counterparts based on SCSP and MCMP transmissions.

Information-based measure of nonlocality

Quantum nonlocality concerns correlations among spatially separated systems that cannot be explained classically without communication among the parties. Thus, a natural measure of nonlocal correlations is provided by the minimal amount of communication required for classically simulating them. In this paper, we present a method to compute the minimal communication cost of parallel simulations, which we call nonlocal capacity, for any general nonsignaling correlations. This measure turns out to have an important role in communication complexity and can be used to discriminate between local and nonlocal correlations, as an alternative to the violation of Bell’s inequalities.

A General Communication Cost Optimization Framework for Big Data Stream Processing in Geo-Distributed Data Centers

With the explosion of big data, processing large numbers of continuous data streams, i.e., big data stream processing (BDSP), has become a crucial requirement for many scientific and industrial applications in recent years. By offering a pool of computation, communication and storage resources, public clouds, like Amazon's EC2, are undoubtedly the most efficient platforms to meet the ever-growing needs of BDSP. Public cloud service providers usually operate a number of geo-distributed datacenters across the globe. Different datacenter pairs are with different inter-datacenter network costs charged by Internet Service Providers (ISPs). While, inter-datacenter traffic in BDSP constitutes a large portion of a cloud provider's traffic demand over the Internet and incurs substantial communication cost, which may even become the dominant operational expenditure factor. As the datacenter resources are provided in a virtualized way, the virtual machines (VMs) for stream processing tasks can be freely deployed onto any datacenters, provided that the Service Level Agreement (SLA, e.g., quality-of-information) is obeyed. This raises the opportunity, but also a challenge, to explore the inter-datacenter network cost diversities to optimize both VM placement and load balancing towards network cost minimization with guaranteed SLA. In this paper, we first propose a general modeling framework that describes all representative inter-task relationship semantics in BDSP. Based on our novel framework, we then formulate the communication cost minimization problem for BDSP into a mixed-integer linear programming (MILP) problem and prove it to be NP-hard. We then propose a computation-efficient solution based on MILP. The high efficiency of our proposal is validated by extensive simulation based studies.

Trainable and Low-Cost SMO Pattern Classifier Implemented via MCMC and SFBS Technologies

This paper presents a multicore and multichannel (MCMC) technology and a synchronous and forward-backward scheduling (SFBS) for the cost reduction of sequential minimal optimization trainable pattern classifier. The MCMC technology uses multiple processing cores that are self-reconfigurable and preconfigurable. For different functions, five self-configurable modes and four preconfigurable modes can be combined to achieve high flexibility. A multichannel hierarchical architecture enables different transfer rates. To minimize communication cost, the SFBS uses synchronous and forward-backward counting for data scheduling. For implementation in reconfigurable FPGAs, MCMC and SFBS are combined for use in synthesis, placement, and routing. Compared with the baseline design, the emulation results show that the proposed architecture has a low area and low power costs (5755 logic elements and 195 mW), respectively. The experimental results confirm the cost improvement achieved by the proposed architecture and methods.

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.

Communication-Efficient Decentralized Event Monitoring in Wireless Sensor Networks

In this paper, we consider monitoring multiple events in a sensing field using a large-scale wireless sensor network (WSN). The goal is to develop communication-efficient algorithms that are scalable to the network size. Exploiting the sparse nature of the events, we formulate the event monitoring task as an $\ell _1$ regularized nonnegative least squares problem where the optimization variable is a sparse vector representing the locations and magnitudes of events. Traditionally the problem can be reformulated by letting each sensor hold a local copy of the event vector and imposing consensus constraints on the local copies, and solved by decentralizedalgorithms such as the alternating direction method of multipliers (ADMM). This technique requires each sensor to exchange theirestimates of the entire sparse vector and hence leads to high communication cost. Motivated by the observation that an event usually has limited influence range, we develop two communication-efficient decentralized algorithms, one is the partial consensus algorithm and the other is the Jacobi approach. In the partial consensus algorithm that is based on the ADMM, each sensor is responsible for recovering those events relevant to itself, and hence only consent with neighboring nodes on a part of the sparse vector. This strategy greatly reduces the amount of information exchanged among sensors. The Jacobi approach addresses the case that each sensor cares about the event occurring at its own position. Jacobi-like iterates are shown to be much faster than other algorithms, and incur minimal communication cost per iteration. Simulation results validate the effectiveness of the proposed algorithms and demonstrate the importance of proper modelling in designing communication-efficient decentralized algorithms.

An efficient scheme for probabilistic skyline queries over distributed uncertain data

Uncertain data has already widely existed in many practical applications, such as sensor networks, RFID networks, location-based services and mobile object management, etc. The skyline queries over uncertain data as an important aspect of uncertain data management, has received extensive attention from the database research community currently, due to its importance in many application including multi-criteria decision making, preference answering, market analysis, etc. However, in most uncertainty applications, the uncertain data are usually collected from vast number of independent data sources among geographically scattered sites, which makes the central assembly of data at one location for storage and query is infeasible and inefficient. Taking account of the network delay and limited bandwidth associated with sharing and communicating large amounts of distributed data over an internet, an important and challenging problem in the scenario is to retrieve all the global skyline tuples from all the distributed local sites with minimum communication cost. In this paper, we propose GFS, which is an efficient scheme for probabilistic skyline over distributed uncertain data. GFS firstly prunes the unqualified tuples with the global grid information and further iteratively prune the unqualified tuples with an improved feedback mechanism. Extensive experiments confirm that the effectiveness and the efficiency of the GFS scheme.

Tracking frequent items over distributed probabilistic data

Tracking frequent items (also called heavy hitters) is one of the most fundamental queries in real-time data due to its wide applications, such as logistics monitoring, association rule based analysis, etc. Recently, with the growing popularity of Internet of Things (IoT) and pervasive computing, a large amount of real-time data is usually collected from multiple sources in a distributed environment. Unfortunately, data collected from each source is often uncertain due to various factors: imprecise reading, data integration from multiple sources (or versions), transmission errors, etc. In addition, due to network delay and limited by the economic budget associated with large-scale data communication over a distributed network, an essential problem is to track the global frequent items from all distributed uncertain data sites with the minimum communication cost. In this paper, we focus on the problem of tracking distributed probabilistic frequent items (TDPF). Specifically, given k distributed sites S = {S1, ? , Sk}, each of which is associated with an uncertain database ??i$\mathcal {D}_{i}$ of size ni, a centralized server (or called a coordinator) H, a minimum support ratio r, and a probabilistic threshold t, we are required to find a set of items with minimum communication cost, each item X of which satisfies Pr(sup(X) ? r × N) >t, where sup(X) is a random variable to describe the support of X and N=?i=1kni$N={\sum }_{i=1}^{k}n_{i}$. In order to reduce the communication cost, we propose a local threshold-based deterministic algorithm and a sketch-based sampling approximate algorithm, respectively. The effectiveness and efficiency of the proposed algorithms are verified with extensive experiments on both real and synthetic uncertain datasets.

Multiarea Distribution System State Estimation

This paper presents a new approach to the distribution system state estimation in wide-area networks. The main goal of this paper is to present a two-step procedure designed to accurately estimate the status of a large-scale distribution network, relying on a distributed measurement system in a multiarea framework. First of all, the network is divided into subareas, according to geographical and/or topological constraints and depending on the available measurement system. Then, in the first step of the estimation process, for each area, a dedicated estimator is used, exploiting all the measurement devices available on the field. In the second step, data provided by local estimators are further processed to refine the knowledge on the operating conditions of the network. To improve the accuracy of the estimation results, correlation arising in the first step estimations has to be suitably evaluated and considered during the second step. Performed analysis shows that existing correlations can be included in the estimation process with very low data exchange among areas, thus involving minimum communication costs. Both first and second steps can be performed in a decentralized way and with parallel processing, thus leading to reduced overall execution times. Test results, obtained on the 123-bus IEEE test network and proving the goodness of the proposed method, are presented and discussed.

SECURE AND PRIVACY PRESERVING DATA CENTRIC SENSOR NETWORKS WITH MULTI-QUERY OPTIMIZATION

In large-scale Wireless Sensor Networks the effective use of the vast amounts of data gathered will require scalable, selforganizing, and energy-efficient data dissemination and data retrieval techniques. Data Centric Sensor (DCS) networks is a better approach in which the sensor nodes send the sensed data to a designated sensor node whose name is associated with sensed data. However, due to unattended nature of Wireless Sensor Networks, these sensor nodes are susceptible to different types of attacks. The attacker may compromise these storage nodes and get data of his interest. In this paper we propose a Secure and Privacy Preserving Data Centric Sensor Networks that includes security and privacy support to DCS networks. We use multi level key structure and cryptographic algorithms to provide security. In addition, we propose a multi-query optimization technique that aggregates similar queries for a small periodic time and construct a query message. This reduces the number of messages required to serve multiple similar queries. Simulation and experimental results show that our work provides a secure privacy preserving data centric sensor network based on cryptographic keys and reduces the message overhead and incurs a minimum communication cost compared to previous works

Lightweight security scheme for IoT applications using CoAP

Purpose – The purpose of this paper is to study lightweight security scheme for Internet of Things (IoT) applications using Constrained Application Protocol (CoAP). Resource-constrained characteristics of IoT systems have ushered in compelling requirements for lightweight application protocol and security suites. CoAP has already been established as the candidate protocol for IoT systems. However, low overhead security scheme for CoAP is still an open problem. Existing security solutions like Datagram Transport Layer Security (DTLS) is not suitable, particularly due to its expensive handshaking, public key infrastructure (PKI)-based authentication and lengthy ciphersuite agreement process. Design/methodology/approach – This paper proposes a lightweight security scheme in CoAP using Advanced Encryption Standard (AES) 128 symmetric key algorithm. The paper presents an object security (payload embedded)-based robust authentication mechanism with integrated key management. The paper introduces few unique modifications to CoAP header to optimize security operation and minimize communication cost. Findings – It is resilient to number of security attacks like replay attack, meet-in-the-middle attack and secure under chosen plaintext attack. This scheme is generic in nature, applicable for gamut of IoT applications. The paper proves efficacy of our proposed scheme for vehicle tracking application in emulated laboratory setup. Specifically, it compares with DTLS-enabled CoAP to establish the lightweight feature of our proposed solution. Research limitations/implications – This paper mainly focuses on implementing in-vehicle tracking systems as an IoT application and used CoAP as the application protocol. Practical implications – Such a lightweight security scheme would provide immense benefit in IoT systems so that resource constraint-sensing devices and nodes can be made secure. This would impact IoT eco systems to a large extent. Originality/value – Such kind of security suite that provides both robustness and lightweight feature is hitherto not known to the authors, particularly in CoAP for IoT applications.

A Study on Energy Optimum Algorithm of LEACH under Three-Dimensional Space

With the constant enlargement of the range of application of wireless sensor network, extending wireless sensor network’ life cycle has attracted more and more attention under the circumstances of limited node energy. On account of three-dimensional space background for the environmental monitoring of high buildings, this thesis put forward an improved energy-saving optimum algorithm of LEACH: improving probability of high-energy node elected as cluster head during cluster head election; selecting a network with the minimum communication cost. The simulation experiment indicates that under the layout of three-dimensional space, improving algorithm makes the load of wireless sensor network more balancing, and extending network’ life cycle. what’ s more, simulation result, in a way, serves as theoretical basis for a further study on the life cycle of wireless sensor network.

Towards a Greener Cloud Infrastructure Management using Optimized Placement Policies

Cloud infrastructures are designed to simultaneously service many, diverse applications that consist of collections of Virtual Machines (VMs). The placement policy used to map applications onto physical servers has important effects in terms of application performance and resource efficiency. We propose enhancing placement policies with network-aware optimizations, trying to simultaneously improve application performance, resource efficiency and power efficiency. The per-application placement decision is formulated as a bi-objective optimization problem (minimizing communication cost and the number of physical servers on which an application runs) whose solution is searched using evolutionary techniques. We have tested three multi-objective optimization algorithms with problem-specific crossover and mutation operators. Simulation-based experiments demonstrate how, in comparison with classic placement techniques, a low-cost optimization results in improved assignments of resources, making applications run faster and reducing the energy consumed by the data center. This is beneficial for both cloud clients and cloud providers.

An Achievable Rate Region for Joint Compression and Dispersive Information Routing for Networks

This paper considers the problem of minimum cost communication of correlated sources over a network with multiple sinks, which consists of distributed source coding followed by routing. We introduce a new routing paradigm called dispersive information routing (DIR), wherein the intermediate nodes are allowed to split a packet and forward subsets of the received bits on each of the forward paths. This paradigm opens up a rich class of research problems, which focus on the interplay between encoding and routing in a network. Unlike conventional routing methods such as in [1], DIR ensures that each sink receives just the information needed to reconstruct the sources it is required to reproduce. We demonstrate using simple examples that the proposed approach offers better asymptotic performance than conventional routing techniques. We show that, under certain assumptions on the cost function, the problem of finding the minimum cost under DIR essentially reduces to characterizing an achievable rate region for a new multiterminal information theoretic setup. While it is possible to derive an achievable region for this setup using prior results from general multiterminal source coding [3], these techniques do not exploit the underlying problem structure and thereby lead to suboptimal regions. In this paper, we propose a new coding scheme, using principles from multiple descriptions encoding [2], and show that it strictly improves upon a corresponding variant of coding scheme in [3]. We further show that the new coding scheme achieves the complete rate region for certain special cases of the general setup and thereby achieves the minimum communication cost under this routing paradigm.